PMID- 36765642
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230213
IS  - 2072-6694 (Print)
IS  - 2072-6694 (Electronic)
IS  - 2072-6694 (Linking)
VI  - 15
IP  - 3
DP  - 2023 Jan 22
TI  - Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling 
      with Supervised Machine Learning.
LID - 10.3390/cancers15030681 [doi]
LID - 681
AB  - Breast cancer is one of the most common invasive cancers in women and it 
      continues to be a worldwide medical problem since the number of cases has 
      significantly increased over the past decade. Breast cancer is the second leading 
      cause of death from cancer in women. The early detection of breast cancer can 
      save human life but the traditional approach for detecting breast cancer disease 
      needs various laboratory tests involving medical experts. To reduce human error 
      and speed up breast cancer detection, an automatic system is required that would 
      perform the diagnosis accurately and timely. Despite the research efforts for 
      automated systems for cancer detection, a wide gap exists between the desired and 
      provided accuracy of current approaches. To overcome this issue, this research 
      proposes an approach for breast cancer prediction by selecting the best fine 
      needle aspiration features. To enhance the prediction accuracy, several feature 
      selection techniques are applied to analyze their efficacy, such as principal 
      component analysis, singular vector decomposition, and chi-square (Chi2). 
      Extensive experiments are performed with different features and different set 
      sizes of features to investigate the optimal feature set. Additionally, the 
      influence of imbalanced and balanced data using the SMOTE approach is 
      investigated. Six classifiers including random forest, support vector machine, 
      gradient boosting machine, logistic regression, multilayer perceptron, and 
      K-nearest neighbors (KNN) are tuned to achieve increased classification accuracy. 
      Results indicate that KNN outperforms all other classifiers on the used dataset 
      with 20 features using SVD and with the 15 most important features using a PCA 
      with a 100% accuracy score.
FAU - Shafique, Rahman
AU  - Shafique R
AUID- ORCID: 0000-0001-7641-2835
AD  - Department of Information and Communication Engineering, Yeungnam University, 
      Gyeongsan 38541, Republic of Korea.
FAU - Rustam, Furqan
AU  - Rustam F
AUID- ORCID: 0000-0001-8403-1047
AD  - School of Computer Science, University College Dublin, D04 V1W8 Dublin, Ireland.
FAU - Choi, Gyu Sang
AU  - Choi GS
AUID- ORCID: 0000-0002-0854-768X
AD  - Department of Information and Communication Engineering, Yeungnam University, 
      Gyeongsan 38541, Republic of Korea.
FAU - Diez, Isabel de la Torre
AU  - Diez IT
AUID- ORCID: 0000-0003-3134-7720
AD  - Department of Signal Theory and Communications and Telematic Engineering, 
      University of Valladolid, Paseo de Belen 15, 47011 Valladolid, Spain.
FAU - Mahmood, Arif
AU  - Mahmood A
AD  - Department of Computer Science & Information Technology, The Islamia University 
      of Bahawalpur, Bahawalpur 63100, Punjab, Pakistan.
FAU - Lipari, Vivian
AU  - Lipari V
AD  - Research Group on Foods, Nutritional Biochemistry and Health, Universidad Europea 
      del Atlantico, Isabel Torres 21, 39011 Santander, Spain.
AD  - Department of Project Management, Universidad Internacional Iberoamericana, 
      Campeche 24560, Mexico.
AD  - Fundacion Universitaria Internacional de Colombia Bogota, Bogota 11001, Colombia.
FAU - Velasco, Carmen Lili Rodriguez
AU  - Velasco CLR
AD  - Research Group on Foods, Nutritional Biochemistry and Health, Universidad Europea 
      del Atlantico, Isabel Torres 21, 39011 Santander, Spain.
AD  - Department of Project Management, Universidad Internacional Iberoamericana 
      Arecibo, Arecibo, PR 00613, USA.
AD  - Project Management, Universidade Internacional do Cuanza, Cuito EN250, Bie, 
      Angola.
FAU - Ashraf, Imran
AU  - Ashraf I
AUID- ORCID: 0000-0002-8271-6496
AD  - Department of Information and Communication Engineering, Yeungnam University, 
      Gyeongsan 38541, Republic of Korea.
LA  - eng
GR  - N/A/European University of the Atlantic/
PT  - Journal Article
DEP - 20230122
PL  - Switzerland
TA  - Cancers (Basel)
JT  - Cancers
JID - 101526829
PMC - PMC9913345
OTO - NOTNLM
OT  - breast cancer prediction
OT  - deep learning
OT  - feature selection
OT  - fine-needle aspiration features
OT  - principal component analysis
OT  - singular value decomposition
COIS- The authors declare no conflict of interest.
EDAT- 2023/02/12 06:00
MHDA- 2023/02/12 06:01
PMCR- 2023/01/22
CRDT- 2023/02/11 01:03
PHST- 2022/12/20 00:00 [received]
PHST- 2023/01/13 00:00 [revised]
PHST- 2023/01/17 00:00 [accepted]
PHST- 2023/02/11 01:03 [entrez]
PHST- 2023/02/12 06:00 [pubmed]
PHST- 2023/02/12 06:01 [medline]
PHST- 2023/01/22 00:00 [pmc-release]
AID - cancers15030681 [pii]
AID - cancers-15-00681 [pii]
AID - 10.3390/cancers15030681 [doi]
PST - epublish
SO  - Cancers (Basel). 2023 Jan 22;15(3):681. doi: 10.3390/cancers15030681.